scholarly journals Applying the Intelligent Decision Heuristic to Solve Large Scale Technician and Task Scheduling Problems

2017 ◽  
pp. 71-81
Author(s):  
Amy Khalfay ◽  
Alan Crispin ◽  
Keeley Crockett
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Scheduling Problems ◽  
Intelligent Decision ◽  
And Task

An Efficient Resources Management Model and Task Scheduling Algorithm in Grid Computing

2011 ◽  
Vol 50-51 ◽  
pp. 526-530 ◽  
Author(s):  
Xiao Bo Gao
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Resource Sharing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Management Model ◽  
Network Computing ◽  
Resources Management ◽  
Task Scheduling Algorithm ◽  
And Task

From the perspective of resource sharing, grid computing is a system ranging from small kind of network system for home using to large-scale network computing systems even to the Internet. The management of resources in the grid environment becomes very complex as these resources are distributed geographically, heterogeneous in nature, and each having their own resource management policies and different access as well as cost models. In this paper, we bring forward an efficient resources management model and task scheduling algorithm in grid computing. The simulation results show that the proposed algorithm achieves resource load balancing, and can be applied to the optimization of task scheduling successfully.

An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Raj Kumar Kalimuthu ◽  
Brindha Thomas
Keyword(s):  
Resource Allocation ◽  
Task Scheduling ◽  
State Of The Art ◽  
Particle Swarm ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Average Response Time ◽  
Multiple Sources ◽  
Computing Paradigm ◽  
And Task

In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.

Algorithms for large scale Shift Minimisation Personnel Task Scheduling Problems

2012 ◽  
Vol 219 (1) ◽  
pp. 34-48 ◽  
Author(s):  
M. Krishnamoorthy ◽  
A.T. Ernst ◽  
D. Baatar
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Scheduling Problems ◽  
Scale Shift

Fast Pruning Algorithm and Task Scheduling under Map/Reduce

2020 ◽  
Vol 16 (10) ◽  
pp. 1627
Author(s):  
Pei Shujun ◽  
Zhang Yu ◽  
Liang Chao
Keyword(s):  
Task Scheduling ◽  
Map Reduce ◽  
Pruning Algorithm ◽  
And Task
Sign in / Sign up

Export Citation Format

Share Document